1. Program Comprehension & Software Evolution - [Lightweight] Principles and [real] Practice Michele Lanza Faculty of Informatics University of Lugano Switzerland

2. 1 Prologue Reverse engineer 1’200’000 lines of C++ code in ca. 2300 classes * 2 = 2’400’000 seconds / 3600 = 667 hours 667 hours / 8 = 83 working days 83 days / 5 = 16 working weeks and 3 days ~ 4 months Questions: –What is the size and the overall structure of the system? –What is the internal structure of the system and its elements? –How did the software system become like that? Once upon a time…

3. 2 The Life Cycle of Software Systems Issues Tool support Scalability Flexibility ? Time Requirements Analysis Design Implementation

4. 3 Object-Oriented Reverse Engineering Goal: take a ( large legacy) software system and “understand” it, i.e., construct a mental model of the system Problem: the software system in question is –Unknown, very large, and complex –Domain- and language-specific –Seldom documented or commented –“In bad shape” ? ?

5. 4 Object-Oriented Reverse Engineering (II) Constructing a mental model requires information about the system: –Top-down approaches –Bottom-up approaches – Mixed Approaches There is no “silver bullet” methodology Every reverse engineering situation is unique Need for flexibility, customizability, scalability, and simplicity ?

6. 5 Reverse Engineering Approaches Reading (source code, documentation, UML diagrams, comments) Running the SW and analyze its execution trace Interview users and developers (if available) Clustering Concept Analysis Software Visualization Software Metrics Slicing and Dicing Querying (Database) Data Mining Logic Reasoning … ?

7. 6 The “Information Crystallization” Problem Many approaches generate too much or not enough information The reverse engineer must make sense of this information by himself We need the right information at the right time ?

8. 7..take a step back..block the ground..think about it.. The information needed to reverse engineer a legacy software system resides at various levels We need to obtain and combine –Coarse-grained information about the whole system –Fine-grained information about specific parts –Evolutionary information about the past of the system !

9. 8 Contents Polymetric Views Software Visualization vs. Reverse Engineering –Coarse-grained –Fine-grained –Evolutionary –Dynamic Information Discussion Demos

10. 9 A Solution - The Polymetric View A lightweight combination of two approaches: –Software visualization (reduction of complexity, intuitive) –Software metrics (scalability, assessment) Interactivity (iterative process, silver bullet impossible) Does not replace other techniques, it complements them: –“Opportunistic code reading”

11. 10 The Polymetric View - Principles Visualize software: –entities as rectangles –relationships as edges Enrich these visualizations: –Map up to 5 software metrics on a 2D figure –Map other kinds of semantic information on nominal colors width metric height metric 2 position metrics Entities Relationships color metric

12. 11 The Polymetric View - Example Nodes = Classes Edges = Inheritance Relationships Width = Number of Attributes Height = Number of Methods Color = Number of Lines of Code … System Complexity View

13. 12 The Polymetric View - Example (II) … Get an impression (build a first raw mental model) of the system, know the size, structure, and complexity of the system in terms of classes and inheritance hierarchies Locate important (domain model) hierarchies, see if there are any deep, nested hierarchies Locate large classes (standalone, within inheritance hierarchy), locate stateful classes and classes with behaviour Count the classes, look at the displayed nodes, count the hierarchies Search for node hierarchies, look at the size and shape of hierarchies, examine the structure of hierarchies Search big nodes, note their position, look for tall nodes, look for wide nodes, look for dark nodes, compare their size and shape, “read” their name => opportunistic code reading System Complexity View Reverse engineering goalsView-supported tasks Nodes = Classes Edges = Inheritance Relationships Width = # attributes Height = # methods Color = # lines of code

14. 13 Structural Specification Target...... Scope.......... Metrics....................................... Layout............ Description................................................................................. Goals……………………………………….. …………………………… Symptoms…………………….. …………………………… Scenario Case Study……………………………………….. ……………………….. The Polymetric View - Description Every polymetric view is described according to a common pattern Every view targets specific reverse engineering goals The polymetric views are implemented in CodeCrawler System Complexity View …

15. 14 Coarse-grained Software Visualization Reverse engineering question: –What is the size and the overall structure of the system? Coarse-grained reverse engineering goals: –Gain an overview in terms of size, complexity, and structure –Asses the overall quality of the system –Locate and understand important (domain model) hierarchies –Identify large classes, exceptional methods, dead code, etc. –…

16. 15 Coarse-grained Polymetric Views - Example Method Efficiency Correlation View Nodes:Methods Edges:- Size: Number of method parameters Position X: Number of lines of code Position Y: Number of statements LOC NOS Goals: Detect overly long methods Detect “dead” code Detect badly formatted methods Get an impression of the system in terms of coding style Know the size of the system in # methods

17. 16 CodeCrawler Demo

18. 17 Clustering the Polymetric Views First Contact System Hotspots System Complexity Root Class Detection Implementation Weight Distribution Candidate Detection Data Storage Class Detection Method Efficiency Correlation Direct Attribute Access View Method Length Distribution Inheritance Assessment Inheritance Classification Inheritance Carrier Intermediate Abstract Class Internal The Class Blueprint

19. 18 Coarse-grained SV - Conclusions Benefits –Views are customizable (context…) and easily modifiable –Simple approach, yet powerful –Scalability Limits –Visual language must be learned

20. 19 Fine-grained Software Visualization Reverse engineering question: –What is the internal structure of the system and its elements? Fine-grained reverse engineering goals: –Understand the internal implementation of classes and class hierarchies –Detect coding patterns and inconsistencies –Understand class/subclass roles –Identify key methods in a class –…

21. 20 The Class Blueprint - Principles Invocation Sequence InitializationExternal InterfaceInternal ImplementationAccessorAttribute The class is divided into 5 layers Nodes Methods, Attributes, Classes Edges Invocation, Access, Inheritance The method nodes are positioned according to Layer Invocation sequence

22. 21 The Class Blueprint - Principles (II) Attribute Read Accessor Delegating Method Constant MethodAbstract Method Overriding Method Extending Method Write Accessor Method # invocations # lines Attribute # external accesses # internal accesses Direct Attribute AccessMethod Invocation

23. 22 The Class Blueprint - Example Delegate: –Delegates functionality to other classes –May act as a “Façade” (DP) Large Implementation: –Deep invocation structure –Several methods –High decomposition Wide Interface Direct Access Sharing Entries

24. 23 The Class Blueprint - A Pattern Language? The patterns reveal information about –Coding style –Coding policies –Particularities We grouped them according to –Size –Layer distribution –Semantics –Call-flow –State usage Moreover… –Inheritance Context –Frequent pattern combinations –Rare pattern combinations They are all part of a pattern language

25. 24 The Class Blueprint - Example (II) Call-flow –Double Single Entry –(=> split class?) Inheritance –Adder –Interface overriders Semantics –Direct Access State Usage –Sharing Entries

26. 25 The Class Blueprint - What do we see?

27. 26 CodeCrawler Demo

28. 27 Fine-grained SV - Conclusions Benefits –Complexity reduction –Visual code inspection technique –Complements the coarse-grained views Limits –Visual language must be learned –Good object-oriented knowledge required –No information about actual functionality => opportunistic code reading necessary

29. 28 Evolutionary Software Visualization Reverse engineering question: –How did the software system become like that? Evolutionary reverse engineering goals: –Understand the evolution of OO systems in terms of size and growth rate –Understand at which time an element, e.g., a class, has been added or removed from the system –Understand the evolution of single classes –Detect patterns in the evolution of classes –…

30. 29 The Evolution Matrix - Principles Last VersionFirst Version Removed Classes Time (Versions) Growth PhaseStagnation Phase Added Classes Version 2.. Version (n - 1)

31. 30 The Evolution Matrix - Principles (II) The Evolution Matrix reveals patterns –The evolution of the whole system (versions, growth and stagnation phases, growth rate, initial and final size) –The life-time of classes (addition, removal) Moreover, we enrich the evolution matrix view with metric information This allows us to see patterns in the evolution of classes Class # methods # attributes

32. 31 The Evolution Matrix - Pattern Language Repeated Modifications make it grow and shrink. System Hotspot: Nearly every new system version requires changes. No “cheap class” Pulsar Supernova Suddenly increases in size, possible reasons: Massive shift of functionality towards a class. Data storage class Developers knew what to fill in. Time (Versions)

33. 32 The Evolution Matrix - Pattern Language (II) Lost the functionality it had and now trundles along without real meaning. Possibly dead code. White Dwarf Red Giant A permanent god class which is always very large Idle Keeps size over several versions. Possibly dead code, possibly good code. Time (Versions)

34. 33 The Evolution Matrix - Pattern Language (III) Exists during only one or two versions. Perhaps an idea which was tried out and then dropped. Has the same lifespan as the whole system. Part of the original design. Perhaps holy dead code which no one dares to remove. Persistent Dayfly

35. 34 The Evolution Matrix - Example

36. 35 Evolutionary Software Visualization - Demo

37. 36 Evolutionary SV - Conclusions Benefits –Complexity reduction Limits –Scalability (can be solved) –Rename problem (can be solved) –Relative changes hard to see (can be solved)

38. 37 Run-Time Analysis - Problems and Challenges RTA and Reverse Engineering - useful (in combination with static information)? Procedural RTA vs. Object-Oriented RTA OO RTA - Conceptual problems –Polymorphism and late-binding –Inheritance and incremental class definition –Functionality (features) spread over the system –Which trace to generate? How? Technical challenges and constraints –Instrumentation problem (logging, VM patching, wrapping,..) –Amount, density, and noise of generated information (Thousands of events in a few seconds..) –Granularity of information (object instantiations, message sends, attribute accesses,..) –How much can we automate?

39. 38 RTA - Questions Can we merge the dynamic information with static information? Can we use a ‘’successful’’ static technique like polymetric views in RTA? –What are the most instantiated classes? –Are there any singletons? –Which classes are object factories? –What is the percentage of actually used methods in classes? –Memory consumption? –Speed bottlenecks? –…

40. 39 Case Study and Experiment Setup Case Study: Moose, our reengineering environment –Implementation language: Smalltalk –Age: 6 years –Size: >250 classes and >3500 methods and a test suite of more than 280 unit tests (a veritable legacy system ;-) Setup –Code instrumentation using MethodWrappers –Trace Scenario(s) given by the Unit test suite –Wrapping down to method body level During trace-time we record events and increase counters Afterwards we map the counter values as metrics

41. 40 Run-time Measurements NCM, the number called methods NMI, the number of method invocations NCI, the number of created instances, that is the number of times a class has been instantiated NCO, the number of created objects, that is the number of ‘foreign’ objects that a class’s objects instantiated Condensed information leads to greater scalability Tradeoff with granularity and sequence of a trace Interval of the values can be great (logarithmic scaling useful)

42. 41 Instance Usage Overview Nodes Classes Edges Inheritance Metric Scale Logarithmic Layout Tree Node Width # of Created Instances Node Height # of Called Methods Node Color # of Method Invocations Symptoms Small, light : unused Narrow, tall: few, but used, instances Flat, pale: heavily instantiated, seldom used Flat, dark: heavily instantiated, functionality partially but heavily used Classes A: CDIFScanner B: AttributeDescription (3500 instances, 350’000 calls!) C: FAMIX metamodel root G: Uninstantiated FAMIX classes (!) I: Smalltalk AST Visitor hierarchy

43. 42 Creation Interaction View Nodes Classes Edges Instantiation Metric Scale Logarithmic Layout Embedded Spring Node Width # of Created Objects Node Height # of Created Instances Node Color # of Created Instances Edge Width # of Instantiations Symptoms Unconnected : uninstantiated Connected, small: classes with few instances Flat, light: instance creators, seldom instantiated, possibly factories Narrow, dark: heavily instantiated, but do not create many other instances Wide, dark: heavily instantiated and used Class Examples A: AttributeDescription - C: VWImporter (high-level import), D: VWParseTreeEnumerator (low-level import) E: FAMIXClass,..Method,..Attribute, etc. F: FAMIXAccess, FAMIXInvocation - G: MSEMeasurement (short-lived objects)

44. 43 Dynamic Information SV - Conclusions Pros –Some new views on software systems –Intuitive and compact way of presenting very large amounts of information –Insights into implementation issues –Side result: assessment of test suite Cons –Loss of granularity and order –Suitability for optimization domain unclear –Probably does not really scale up for very large systems (but this depends on the viewer and his/her will to interact..) –The current approach is intrinsically interactive (automatisation would be possible using advanced metrics-based techniques like detection strategies)

45. 44 What about reality? Most IDEs have no or limited visualization support Not an industry “standard”, most developers still have vi & emacs mentality Still poor usability –May be used as “stand-alone” browsing tool, but not as part of a development metholodogy –Needs much more effort (and people) to be “sexy” Ongoing work must cope with the “present hypes”, such as distributed development, eXtreme programming, etc.

46. 45 Epilogue Did we succeed after all? Not completely, but… –System Hotspots View on 1.200’000 LOC of C++ –System Complexity View on ca. 200 classes of C++ The End

47. 46 Industrial Validation - The Acid Test Several large, industrial case studies (NDA) Different implementation languages Severe time constraints SystemLanguageLines of CodeClasses ZC++1’200’000~2300 YC++/Java120’000~400 XSmalltalk600’000~2500 WCOBOL40’000- SortieC/C++28’000~70 DuplocSmalltalk32’000~230 JunSmalltalk135’000~700 ArgoUMLJava220’000~1400

48. 47 Questions and Comments Let’s do it…